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EN
Purpose. Existing knowledge about the appearance, thickness, and chemical composition of 37 phosphate coatings on titanium inside porous structures is insufficient. Such knowledge is 38 important for the design and fabrication of porous implants. 39 Methods. Metallic scaffolds were fabricated by selective laser melting of 316L stainless steel 40 powder. Phosphate coatings were deposited on Ti sensors placed either outside the scaffolds 41 or in the holes in the scaffolds. The electrochemically-assisted cathodic deposition of 42 phosphate coatings was performed under galvanostatic conditions in an electrolyte containing 43 the calcium and phosphate ions. The phosphate deposits were microscopically investigated; 44 this included the performance of mass weight measurements and chemical analyses of the content of Ca2+ and PO4 2‒ 45 ions after the dissolution of deposits. 46 Results. The thicknesses of the calcium phosphate coatings were about 140 and 200 nm for 47 isolated titanium sensors and 170 and 300 nm for titanium sensors placed inside pores. 48 Deposition of calcium phosphate occurred inside the pores up to 150 mm below the scaffold 49 surface. The deposits were rich in Ca, with a Ca/P ratio ranging between 2 and 2.5. 50 Conclusions. Calcium phosphate coatings can be successfully deposited on a Ti surface 51 inside a model scaffold. An increase in cathodic current results in an increase in coating 52 thickness. Any decrease in the cathodic current inside the porous structure is slight. The 53 calcium phosphate inside the pores has a much higher Ca/P ratio than that of stoichiometric 54 HAp, likely due to a gradual increase in Ca fraction with distance from the surface.
EN
Purpose: The article discusses the use of artificial neural networks for research and prediction of the impact of chemical elements and heat treatment parameters on the mechanical properties of stainless steels optimized by genetic algorithm. Design/methodology/approach: To improve the quality of artificial neural network models and improve their performance the number of input variables of artificial neural networks has been optimized with use of genetic algorithms. Then a computational model build with optimised artificial neural networks were trained and verified. Findings: Optimization, except of tensile strength Rm case, has allowed the development of artificial neural networks, which either showed a better or comparable result from base networks, and also have a reduced number of input variables. As a result, in computational model constructed with use of these networks the noise information is reduced. Research limitations/implications: Data analysis was needed to verify if obtained data used for modelling are relevant to use them in artificial neural networks training processes. Practical implications: The use of artificial intelligence allows the multifaceted development of stainless steels engineering, even if only a small number of descriptors is available. Constructed and optimised computational model build with use of optimised artificial neural networks allows prediction of mechanical properties of rolled ferritic stainless steels after normalization. Originality/value: Introduced model can be obtain in industry to reduce manufacturing costs of materials. It can also simplify material selection, when engineer must properly choose the chemical elements and adequate plastic and/or heat treatment of stainless steels with required mechanical properties.
PL
Podano charakterystykę lin otwartych skręcanych ze stali szlachetnej, otwartych skręcanych powlekanych galwanicznie oraz lin całkowicie zamkniętych. Przedstawiono wyniki badań na rozciąganie lin z zakończeniami na obu końcach. Uzyskano pozytywne rezultaty badań.
EN
The characteristics properties of twistem cables maid of high strength steel wires - bare, with electrolytic coating and fully sealed is presented. Results of strength investigation of cables tension bearing capacity with anchorages on both ends are given.
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